Late Fall Associated Region: Genome Wide Association Study and Marker Selection
Eric C. Anderson
Last Updated: 2025-03-27
Introduction
Here we do a GWAS to find a genomic region that is substantially differentiated between the late-fall and fall runs in the Central Valley, and then we identify several candidate sites to target for amplicon sequencing.
Input and Output Paths
# inputs:
input_list <- list(
chr34_late_fall_mafs = "stored_results/5Mb_Chr34-late-fall.mafs.gz",
chr34_cv_fall_mafs = "stored_results/5Mb_Chr34-cv-fall.mafs.gz",
gwas_lf_v_sanjo = "stored_results/lfar-gwas/lf_v_sanjo-NC_037130.1.lrt0.gz",
gwas_lf_v_frhf = "stored_results/lfar-gwas/lf_v_frhf-NC_037130.1.lrt0.gz",
ots34_snpeff = "stored_results/thompson2020-vcf/ots34-5Mb-snpEff.vcf.gz",
pvals = "stored_results/stored_p_vals.rds"
)
# outputs:
output_list <- list(
lfar_candidates_tex = "tex/images/lfar-candidates.pdf"
)
# additional outputs not specified in Snakefile:
output_list <- c(
output_list,
late_fall_ids = "results/lfar/late-fall-ids.txt",
cv_fall_ids = "results/lfar/cv-fall-ids.txt",
candidates_for_assays = "results/lfar/candidates_for_assays.csv"
)
# you can create the necessary output directories like this:
dump <- lapply(output_list, function(x)
dir.create(dirname(x), recursive = TRUE, showWarnings = FALSE)
)GWAS
This is all executed with a Snakefile and associated files that
reside in the repository at:
SnakemakeSubDirs/cga-late-fall-bam-assoc. The contents of
that directory were originally part of another GitHub repo—simply
eriqande/cga-late-fall-bam-assoc—but have been inserted
here for ease of archiving.
To run that analysis it is necessary to first map the sequences of the salmon (publicly archived at https://www.ncbi.nlm.nih.gov/sra/PRJNA667732) to the RefSeq version of Otsh_v1.0, and name the resulting BAM files as indicated in the bamlists. If you would like to reproduce the analysis, let us know, as we can provide those BAMS.
The snakefile can can be launched something like this:
snakemake -np --use-envmodules --use-conda --cores 24The figure created by that Snakefile is included in the paper. We
also have saved the likelihood ratios for the various markers on
Chromosome 34 (NC_037130.1) for both the Late-fall vs San Joaquin fall
and the Late-Fall vs Feather River fall comparisons in
stored_results so that we can have those when visualizing
allele frequencies.
Additionally, information for all markers with a p-value > 3 in
both the SanJo-vs-LateFall and the FeatherRiverHatcheryFall-vs-LateFall
comparisons was saved in stored_results/stored_p_vals.rds
for use now.
Investigate GWAS pvalues
library(tidyverse)
library(vcfR)
# read in the pvals and label the comparisons FRFH and SanJo and then pivot that
# so we can compare values from the two analyses at each marker
pvals <- read_rds(input_list$pvals) %>%
mutate(comparison = ifelse(comparison == "Late Fall versus Feather River Hatchery Fall", "FRHF", "SanJo")) %>%
select(-(Major:Frequency), -(mh_label:xpos), -cgroup) %>%
pivot_wider(
names_from = comparison,
values_from = c(LRT, neg_log10_p)
)
# here are the total number of SNPs in each comparison:
FRH_snps <- 13477607
SJ_snps <- 12752546
# now, we can look for those that have have log10 pvals in both of the comparisons.
# maybe just plot them first:
ggplot(pvals, aes(x = neg_log10_p_FRHF, y = neg_log10_p_SanJo)) +
geom_point() +
geom_hline(yintercept = -log10(5e-8), linetype = "dashed") +
geom_vline(xintercept = -log10(5e-8), linetype = "dashed")## Warning: Removed 30390 rows containing missing values or values outside the
## scale range (`geom_point()`).
So, that is only a single SNP in both:
pvals %>%
filter(neg_log10_p_FRHF > -log10(5e-8), neg_log10_p_SanJo > -log10(5e-8))Now, let’s consider a Bonferroni correction:
pv2 <- pvals %>%
mutate(
bonFRHF = (10 ^ -neg_log10_p_FRHF) * FRH_snps,
bonSJ = (10 ^ -neg_log10_p_SanJo) * SJ_snps,
) %>%
arrange(bonFRHF)
pv2So, that is only a single SNP that would be “significant” under a Bonferroni correction, in the FRFH comparison.
Likewise only a single SNP is “significant” at a Bonferroni corrected 0.05 level in the SJ comparison, hich we show by sorting the above table on the SanJo values:
pv2 %>%
arrange(bonSJ)Marker Selection
This was originally done in
/Users/eriq/Documents/git-repos/further-chinook-wgs/101-design-late-fall-assays.html
and it has been transferred here, now.
Mac originally ran the association analysis between Late Fall and the two fall run groups in the Central Valley (Feather and San Joaquin), finding significant differences on Chromosome 34 (NC_037130.1). We have seen the same thing with the GWAS just described.
Accordingly, we will investigate a 5 Mb chunk of Chromosome 34 to:
- confirm those markers and explore patterns of differentiation in the region
- See if the differentiation corresponds to annotated variants of large effect
- identify candidate markers that we should be able to type in many more individuals. We will provide sequences flanking these candidates so that Anthony can design primers for them.
Overview of allele frequency differences in the region
We will simply compare the allele frequencies estimated in the CV fall-run vs the late-fall run using ANGSD. I will do this in a 5 Mb chunk for a little context.
Get the IDs
# grab the meta data from the Science paper off of GitHub
nooks <- read_csv("https://raw.githubusercontent.com/eriqande/thompson-et-al-2020-chinook-salmon-migration-timing/master/data/wgs-chinook-samples.csv")
# write out the names of the late-fall fish
nooks %>%
filter(Population == "Coleman Hatchery Late Fall") %>%
pull(vcf_name) %>%
cat(., sep = "\n", file = output_list$late_fall_ids)
# same for the fall-run fish in the CV:
nooks %>%
filter(Population %in% c("Feather River Hatchery Fall", "San Joaquin River Fall")) %>%
pull(vcf_name) %>%
cat(., sep = "\n", file = output_list$cv_fall_ids)Extract 5 Mb and the two sets of indivs
We show the code but don’t actually do it, here.
bcftools view -S outputs/007/late-fall-ids.txt -Oz stored_results/thompson2020-vcf/NC_037130.1.vcf.gz NC_037130.1:1-5000000 > outputs/007/late-fall.vcf.gz
bcftools view -S outputs/007/cv-fall-ids.txt -Oz stored_results/thompson2020-vcf/NC_037130.1.vcf.gz NC_037130.1:1-5000000 > outputs/007/cv-fall.vcf.gzRun ANGSD to estimate allele frequencies
Here is the code used. We don’t do it here, because in and update from angsd 0.920 to 0.938 it broke. But I have the estimated allele freqs in stored results.
cd outputs/007
angsd -vcf-gl late-fall.vcf.gz -fai ../../inputs/Otsh_v1.0_genomic.fna.fai -nind 16 -domaf 3 -out late-fall 2> late-fall.redirect.stderr
angsd -vcf-gl cv-fall.vcf.gz -fai ../../inputs/Otsh_v1.0_genomic.fna.fai -nind 32 -domaf 3 -out cv-fall 2> cv-fall.redirect.stderrRead the estimated allele freqs in and analyse them
af1 <- list(
late_fall = read_tsv(input_list$chr34_late_fall_mafs),
cv_fall = read_tsv(input_list$chr34_cv_fall_mafs)
) %>%
bind_rows(.id = "run") %>%
pivot_wider(names_from = run, values_from = c(knownEM, unknownEM, nInd)) %>%
mutate(abs_diff = abs(knownEM_late_fall - knownEM_cv_fall))Let’s only bother with ones that have 8 or more indivs with reads in late fall and 16 or more in cv-fall, and then we can plot them.
af2 <- af1 %>%
filter(nInd_late_fall >= 8 & nInd_cv_fall >= 16)
ggplot(af2, aes(x = position, y = abs_diff)) +
geom_point(alpha = 0.4)
That is pretty cool. It shows that characteristic pattern near 800 Kb that we saw around GREB1L, where there a lots of SNPs with large allele frequency differences, and lots with very small allele frequency differences, but very few with intermediate differences.
One thing I want to confirm is that all the SNPs with small allele freq differences in that region are actually at low frequency in both groups. We can do that by coloring the points by the max frequency (between the two groups) of the minor allele—that is the allele that is at lowest average frequency in the two populations.
af3 <- af2 %>%
mutate(is_maf = (knownEM_cv_fall + knownEM_late_fall) < 0.5,
max_maf = pmax(ifelse(is_maf, knownEM_late_fall, 1 - knownEM_late_fall),
ifelse(is_maf, knownEM_cv_fall, 1 - knownEM_cv_fall))
)
mmp <- ggplot(af3, aes(x = position, y = abs_diff, color = max_maf)) +
geom_point() +
scale_color_viridis_c()
mmp
And, now, let us also do a version in which the high max_mafs are printed on top, so that we can see them if they exist…
mmp_sort <- ggplot(af3 %>% arrange(max_maf),
aes(x = position, y = abs_diff, color = max_maf)) +
geom_point() +
scale_color_viridis_c()
mmp_sort
Outstanding. It is clear that there is a fairly large haplotype/region that is differentiated between fall and late fall there in the first 2 Mb of the chromosome.
Display some of the highly associated SNPs there
The GWAS turned up a number of highly associated SNPs. Let’s add circles for some of the ones with the largest likelihood ratio statistic.
We will take the top 6 from each of the comparisons:
lfvsj <- read_tsv(input_list$gwas_lf_v_sanjo) %>%
arrange(desc(LRT))
lfvfrhf <- read_tsv(input_list$gwas_lf_v_frhf) %>%
arrange(desc(LRT))
# now get the top 6 from each for illustration
top6_from_each <- list(
lfvsj %>% slice(1:6),
lfvfrhf %>% slice(1:6)
) %>%
bind_rows() %>%
arrange(desc(LRT)) %>%
filter(!duplicated(Position))
# print those out
top6_from_eachSo, add those to the plot
top6freqs <- af3 %>% semi_join(top6_from_each, by = c("position" = "Position"))
mmp_sort +
geom_point(
data = top6freqs,
shape = 21,
size = 3,
color = "black"
)
That shows us that there are a lot of other markers with substantial allele freq diffs in this region, but the association test puts thew big circles up as the most significant, perhaps because of the sample sizes. It sort of reinforces my informal belief that many insights can come from looking at absolute allele freq diffs, at least in these sorts of cases where you have a haplotypic region with very large allele freq differences).
Annotate the variants
Before we start designing assays and things, I want to look for large-effect mutations. We have the annotations for the positions in these 5 Mb of Ots34 in the stored results (from the Science paper), so we will use them here:
First we read in the annotations:
ann_vcf <- read.vcfR(input_list$ots34_snpeff)## Scanning file to determine attributes.
## File attributes:
## meta lines: 14064
## header_line: 14065
## variant count: 52808
## column count: 25
## Meta line 1000 read in.Meta line 2000 read in.Meta line 3000 read in.Meta line 4000 read in.Meta line 5000 read in.Meta line 6000 read in.Meta line 7000 read in.Meta line 8000 read in.Meta line 9000 read in.Meta line 10000 read in.Meta line 11000 read in.Meta line 12000 read in.Meta line 13000 read in.Meta line 14000 read in.Meta line 14064 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 52808
## Character matrix gt cols: 25
## skip: 0
## nrows: 52808
## row_num: 0
## Processed variant 1000Processed variant 2000Processed variant 3000Processed variant 4000Processed variant 5000Processed variant 6000Processed variant 7000Processed variant 8000Processed variant 9000Processed variant 10000Processed variant 11000Processed variant 12000Processed variant 13000Processed variant 14000Processed variant 15000Processed variant 16000Processed variant 17000Processed variant 18000Processed variant 19000Processed variant 20000Processed variant 21000Processed variant 22000Processed variant 23000Processed variant 24000Processed variant 25000Processed variant 26000Processed variant 27000Processed variant 28000Processed variant 29000Processed variant 30000Processed variant 31000Processed variant 32000Processed variant 33000Processed variant 34000Processed variant 35000Processed variant 36000Processed variant 37000Processed variant 38000Processed variant 39000Processed variant 40000Processed variant 41000Processed variant 42000Processed variant 43000Processed variant 44000Processed variant 45000Processed variant 46000Processed variant 47000Processed variant 48000Processed variant 49000Processed variant 50000Processed variant 51000Processed variant 52000Processed variant: 52808
## All variants processedavt <- vcfR2tidy(ann_vcf, info_only = TRUE)
# get column heading for the snpEff output
ann_column_names <- avt$meta %>%
filter(Tag == "INFO", ID == "ANN") %>%
pull(Description) %>%
str_replace_all(., pattern = "^.* '| +|'", "") %>%
str_split(., "\\|") %>%
pluck(1)
anns <- avt$fix %>%
select(CHROM, POS, REF, ALT, ANN) %>%
separate(ANN, into = ann_column_names, sep = "\\|")## Warning: Expected 16 pieces. Additional pieces discarded in 33229 rows [54, 92, 97, 104, 108, 125, 132, 164, 165, 167, 180, 190, 232, 252, 260, 266, 270, 273,
## 280, 283, ...].I don’t think we need to worry about the discarded pieces there.
Now, we can join those annotations onto the allele frequencies and plot them, coloring by predicted effect:
af3_ann <- left_join(af3, anns, by = c("position" = "POS")) %>%
mutate(Annotation_Impact_f = factor(Annotation_Impact,
levels = c("HIGH", "LOW", "MODERATE", "MODIFIER"))) %>%
arrange(desc(Annotation_Impact_f))
imp_col <- ggplot(af3_ann, aes(x = position, y = abs_diff, colour = Annotation_Impact)) +
geom_point() +
scale_colour_manual(values = c(HIGH = "red", LOW = "green", MODERATE = "orange", MODIFIER = "gray90")) +
theme_bw()
imp_col
So, there are a few that we would want to be sure to hit.
Finding candidates for assay design
When I originally did this, I also used a hack to include indels in here. I spare the reader from the gory details of that.
We could rank things by allele frequency difference, but I would also
like to incorporate an estimate of the variance in these absolute
frequency differences. The variance in that difference will be the sum
of the two variances, so it is easy to calculate. We don’t really know
what the sample size is, but I am going to throw out as an approximation
that each individual in the nInd field represents a sample
of about 1.2 gene copies.
We will estimate the variances from each different estimate and then calculate two standard deviations below the actual estimate and have that as a “lower bound” on the absolute value of the allele frequency difference.
# here is a function for computing the lower bound on the absolute alle freq diff
lub <- function(f1, f2, n1, n2, s = 1.2) {
var <- (f1 * (1 - f1) / (n1 * s)) + (f2 * (1 - f2) / (n2 * s))
sd <- sqrt(var)
abs(f1 - f2) - 2 * sd
}
saind2 <- af3_ann %>%
mutate(abs_diff_lb = lub(knownEM_late_fall, knownEM_cv_fall, nInd_late_fall, nInd_cv_fall))Plot those to make sure they look right, and denote the most highly associated ones so that we can see those.
top6again <- saind2 %>%
semi_join(top6_from_each, by = c("position" = "Position"))
ggplot(saind2, aes(x = abs_diff, y = abs_diff_lb, colour = max_maf)) +
geom_point() +
scale_colour_viridis_c() +
geom_point(data = top6again, fill = NA, colour = "black", size = 3, shape = 21)
All right. When I look at that, I think to myself, The ones I would want to design assays for (for distinguishing fall-run from late-fall), would be:
- The ones with the highest association values
- The ones with an abs_diff > 0.75 and an abs_diff “lower bound”
above 0.5.
- The ones with an abs_diff > 0.75 and an abs_diff “lower bound”
above 0.5.
- Then, for any remaining ones we might want to do, I would choose
things with an abs_diff > 0.5, and abs_diff_lb > 0.25, and a
max_maf as close to 1 as possible.
- Then, for any remaining ones we might want to do, I would choose
things with an abs_diff > 0.5, and abs_diff_lb > 0.25, and a
max_maf as close to 1 as possible.
- Annotation_Impact %in% c(“Moderate”, “High”)
We will designate those with a bit mask, with bits being 2 raised to their number minus 1 (as given in the parentheses, above).
Another Category
I will also add another category of candidate, which is out in the
flanks and might be interesting for assessing LD out there.
- Position > 2.2 Mb and abs_diff > 0.48 and abs_diff_lb > 0.25
Mark them.
assay_candi <- saind2 %>%
mutate(candi_category = 1 * position %in% top6_from_each$Position +
2 * (abs_diff > 0.75 & abs_diff_lb > 0.5) +
4 * (abs_diff > 0.5 & abs_diff_lb > 0.25 & max_maf > 0.98) +
8 * (Annotation_Impact %in% c("MODERATE", "HIGH") & (abs_diff > 0.5)) +
16 * (abs_diff > 0.48 & abs_diff_lb > 0.20 & position > 2.2e6)) %>%
select(chromo:max_maf, Annotation_Impact_f:candi_category, everything())
# how many?
assay_candi %>%
count(candi_category)Just to be explicit about what these candi_categories mean:
- 0 : not selected for any reason
- 1 : Hi-assoc (from Mac)
- 2 : abs_diff > 0.75 & abs_diff_lb > 0.5
- 3 : 1 and 2
- 4 : (abs_diff > 0.5 & abs_diff_lb > 0.25 & max_maf > 0.98)
- 7 : 1 and 2 and 4
- 8 : (Annotation_Impact %in% c(“MODERATE”, “HIGH”) & (abs_diff > 0.5)) )
- 16 : abs_diff > 0.48 & abs_diff_lb > 0.20 & position > 2.2e6
- 20 : 4 and 16
Plot them on a plot like the above so we can see them.
ggplot(mapping = aes(x = abs_diff, y = abs_diff_lb, colour = factor(candi_category))) +
geom_point(data = assay_candi %>% filter(candi_category == 0), colour = "gray90") +
geom_point(data = assay_candi %>% filter(candi_category > 0)) +
theme_bw() +
scale_color_brewer(palette = "Dark2")
And see where those candidates live:
ggplot(mapping = aes(x = position, y = abs_diff, colour = factor(candi_category))) +
geom_point(data = assay_candi %>% filter(candi_category == 0), colour = "gray90") +
geom_point(data = assay_candi %>% filter(candi_category > 0)) +
theme_bw() +
scale_color_brewer(palette = "Dark2")
So, we have good candidates across a couple Mb. That is great.
Let’s make a plot of that for the supplement:
assay_candi4fig <- assay_candi %>%
mutate(
candi_cat = case_match(
candi_category,
0 ~ NA_character_,
1 ~ "1: One of the 10 SNPs with lowest p-values",
2 ~ "2: |d| > 0.75 with lower confidence interval >0.5",
3 ~ "3: 1 and 2",
4 ~ "4: |d| > 0.5 with lower CI >0.25 and near fixed in one group",
7 ~ "7: 1 and 2 and 4",
8 ~ "8: snpEff annotation of High or Moderate",
16 ~ "16: |d| > 0.48 with lower CI >0.2 and position >2.2 Mb",
20 ~ "20: 4 and 16"
),
`Candidate Marker Category` = factor(
candi_cat,
levels = c(
"1: One of the 10 SNPs with lowest p-values",
"2: |d| > 0.75 with lower confidence interval >0.5",
"3: 1 and 2",
"4: |d| > 0.5 with lower CI >0.25 and near fixed in one group",
"7: 1 and 2 and 4",
"8: snpEff annotation of High or Moderate",
"16: |d| > 0.48 with lower CI >0.2 and position >2.2 Mb",
"20: 4 and 16"
))
)
g <- ggplot(mapping = aes(x = position, y = abs_diff, colour = `Candidate Marker Category`)) +
geom_point(data = assay_candi4fig %>% filter(candi_category == 0), colour = "gray90") +
geom_point(data = assay_candi4fig %>% filter(candi_category > 0)) +
theme_bw() +
scale_color_brewer(palette = "Dark2") +
xlab("Position on Chromosome 34 (Mb)") +
ylab("|d|, Absolute Difference in Allele Frequency")
ggsave(g, filename = output_list$lfar_candidates, width = 8, height = 5)Preparing an assay order
We prepare assay order information for these 49 good candidates, and handed that to Anthony, along with the sequences, to design primers.
That process involved some custom scripts and Primer3. Once the candidates came back, they were tested in a variety of Chinook samples and it was found that only 8 gave reliable amplicons that mapped reliably, etc. And those are used in the ensuing work.
Here, print out the actual candidates:
actual_candidates <- assay_candi %>%
filter(candi_category > 0) %>%
arrange(position)
# write that out for fun:
write_csv(actual_candidates, file = output_list$candidates_for_assays)Session Info
sessioninfo::session_info()## ─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.3 (2025-02-28)
## os macOS Monterey 12.7.5
## system aarch64, darwin20
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Denver
## date 2025-03-27
## pandoc 3.6.4 @ /Users/eriq/Documents/git-repos/california-chinook-microhaps/.snakemake/conda/def120e971fb2c87a8c042b4c2c09bdb_/bin/ (via rmarkdown)
## quarto 1.4.550 @ /usr/local/bin/quarto
##
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## ! package * version date (UTC) lib source
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##
## [1] /Users/eriq/Documents/git-repos/california-chinook-microhaps/renv/library/macos/R-4.4/aarch64-apple-darwin20
## [2] /Users/eriq/Library/Caches/org.R-project.R/R/renv/sandbox/macos/R-4.4/aarch64-apple-darwin20/f7156815
##
## * ── Packages attached to the search path.
## P ── Loaded and on-disk path mismatch.
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────Running Time
Running the code and rendering this notebook required approximately this much time:
Sys.time() - start_time## Time difference of 16.73026 secs